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Abstract

A multi-layered pattern recognition neural network (30) is disclosed that comprises an input layer (50) that is operable to be mapped onto an input space that includes a scan window (32). Two hidden layers (54) and (58) map the input space to an output layer (34). The hidden layers utilize a local receptor field architecture and store representations of objects within the scan window (32) for mapping into one of a plurality of output nodes. Further, the output layer (34) is also operable to store representations of desired distances between the center of the scan window (32) and the next adjacent object thereto and also the distance between the center of the scan window (32) and the center of the current object. A scanning system can then utilize the information regarding the distance to the next adjacent object, which is stored in an output vector (40) to incrementally jump to the center of the next adjacent character rather than scan the entire distance therebetween. This is referred to as a saccade operation. Once the scan window ( 32) is disposed over the next object, a corrective saccade can be performed by utilizing the information output by the neural network (30) relating to the distance between the center of the scan window (32) and the current character. This information is output as an output vector (38) from the neural network (30).

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First Claim